[docs] Improve SVD pipeline docs (#7087)

* update svd docs

* fix example doc string

* update return type hints/docs

* update type hints

* Fix typos in pipeline_stable_video_diffusion.py

* make style && make fix-copies

* Update src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* update based on suggestion

---------
Co-authored-by: M. Tolga Cangöz <mtcangoz@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py

@@ -21,16 +21,33 @@ import PIL.Image
 import torch
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
-from ...image_processor import VaeImageProcessor
+from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...models import AutoencoderKLTemporalDecoder, UNetSpatioTemporalConditionModel
 from ...schedulers import EulerDiscreteScheduler
-from ...utils import BaseOutput, logging
+from ...utils import BaseOutput, logging, replace_example_docstring
 from ...utils.torch_utils import is_compiled_module, randn_tensor
 from ..pipeline_utils import DiffusionPipeline
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> from diffusers import StableVideoDiffusionPipeline
+        >>> from diffusers.utils import load_image, export_to_video
+
+        >>> pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16")
+        >>> pipe.to("cuda")
+
+        >>> image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd-docstring-example.jpeg")
+        >>> image = image.resize((1024, 576))
+
+        >>> frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0]
+        >>> export_to_video(frames, "generated.mp4", fps=7)
+        ```
+"""
+
 
 def _append_dims(x, target_dims):
     """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
@@ -41,7 +58,7 @@ def _append_dims(x, target_dims):
 
 
 # Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid
-def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"):
+def tensor2vid(video: torch.Tensor, processor: VaeImageProcessor, output_type: str = "np"):
     batch_size, channels, num_frames, height, width = video.shape
     outputs = []
     for batch_idx in range(batch_size):
@@ -65,15 +82,15 @@ def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type:
 @dataclass
 class StableVideoDiffusionPipelineOutput(BaseOutput):
     r"""
-    Output class for zero-shot text-to-video pipeline.
+    Output class for Stable Video Diffusion pipeline.
 
     Args:
-        frames (`[List[PIL.Image.Image]`, `np.ndarray`]):
-            List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
-            num_channels)`.
+        frames (`[List[List[PIL.Image.Image]]`, `np.ndarray`, `torch.FloatTensor`]):
+            List of denoised PIL images of length `batch_size` or numpy array or torch tensor
+            of shape `(batch_size, num_frames, height, width, num_channels)`.
     """
 
-    frames: Union[List[PIL.Image.Image], np.ndarray]
+    frames: Union[List[List[PIL.Image.Image]], np.ndarray, torch.FloatTensor]
 
 
 class StableVideoDiffusionPipeline(DiffusionPipeline):
@@ -119,7 +136,13 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
 
-    def _encode_image(self, image, device, num_videos_per_prompt, do_classifier_free_guidance):
+    def _encode_image(
+        self,
+        image: PipelineImageInput,
+        device: Union[str, torch.device],
+        num_videos_per_prompt: int,
+        do_classifier_free_guidance: bool,
+    ) -> torch.FloatTensor:
         dtype = next(self.image_encoder.parameters()).dtype
 
         if not isinstance(image, torch.Tensor):
@@ -164,9 +187,9 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
     def _encode_vae_image(
         self,
         image: torch.Tensor,
-        device,
-        num_videos_per_prompt,
-        do_classifier_free_guidance,
+        device: Union[str, torch.device],
+        num_videos_per_prompt: int,
+        do_classifier_free_guidance: bool,
     ):
         image = image.to(device=device)
         image_latents = self.vae.encode(image).latent_dist.mode()
@@ -186,13 +209,13 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
 
     def _get_add_time_ids(
         self,
-        fps,
-        motion_bucket_id,
-        noise_aug_strength,
-        dtype,
-        batch_size,
-        num_videos_per_prompt,
-        do_classifier_free_guidance,
+        fps: int,
+        motion_bucket_id: int,
+        noise_aug_strength: float,
+        dtype: torch.dtype,
+        batch_size: int,
+        num_videos_per_prompt: int,
+        do_classifier_free_guidance: bool,
     ):
         add_time_ids = [fps, motion_bucket_id, noise_aug_strength]
 
@@ -212,7 +235,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
 
         return add_time_ids
 
-    def decode_latents(self, latents, num_frames, decode_chunk_size=14):
+    def decode_latents(self, latents: torch.FloatTensor, num_frames: int, decode_chunk_size: int = 14):
         # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width]
         latents = latents.flatten(0, 1)
 
@@ -257,15 +280,15 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
 
     def prepare_latents(
         self,
-        batch_size,
-        num_frames,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        latents=None,
+        batch_size: int,
+        num_frames: int,
+        num_channels_latents: int,
+        height: int,
+        width: int,
+        dtype: torch.dtype,
+        device: Union[str, torch.device],
+        generator: torch.Generator,
+        latents: Optional[torch.FloatTensor] = None,
     ):
         shape = (
             batch_size,
@@ -307,6 +330,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
         return self._num_timesteps
 
     @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
         self,
         image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor],
@@ -333,15 +357,16 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
 
         Args:
             image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
-                Image or images to guide image generation. If you provide a tensor, the expected value range is between `[0,1]`.
+                Image(s) to guide image generation. If you provide a tensor, the expected value range is between `[0, 1]`.
             height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
                 The height in pixels of the generated image.
             width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
                 The width in pixels of the generated image.
             num_frames (`int`, *optional*):
-                The number of video frames to generate. Defaults to 14 for `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt`
+                The number of video frames to generate. Defaults to `self.unet.config.num_frames`
+                (14 for `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt`).
             num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                The number of denoising steps. More denoising steps usually lead to a higher quality video at the
                 expense of slower inference. This parameter is modulated by `strength`.
             min_guidance_scale (`float`, *optional*, defaults to 1.0):
                 The minimum guidance scale. Used for the classifier free guidance with first frame.
@@ -351,29 +376,29 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
                 Frames per second. The rate at which the generated images shall be exported to a video after generation.
                 Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training.
             motion_bucket_id (`int`, *optional*, defaults to 127):
-                The motion bucket ID. Used as conditioning for the generation. The higher the number the more motion will be in the video.
+                Used for conditioning the amount of motion for the generation. The higher the number the more motion
+                will be in the video.
             noise_aug_strength (`float`, *optional*, defaults to 0.02):
                 The amount of noise added to the init image, the higher it is the less the video will look like the init image. Increase it for more motion.
             decode_chunk_size (`int`, *optional*):
-                The number of frames to decode at a time. The higher the chunk size, the higher the temporal consistency
-                between frames, but also the higher the memory consumption. By default, the decoder will decode all frames at once
-                for maximal quality. Reduce `decode_chunk_size` to reduce memory usage.
+                The number of frames to decode at a time. Higher chunk size leads to better temporal consistency at the expense of more memory usage. By default, the decoder decodes all frames at once for maximal
+                quality. For lower memory usage, reduce `decode_chunk_size`.
             num_videos_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
+                The number of videos to generate per prompt.
             generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                 A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                 generation deterministic.
             latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
                 generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                 tensor is generated by sampling using the supplied random `generator`.
             output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+                The output format of the generated image. Choose between `pil`, `np` or `pt`.
             callback_on_step_end (`Callable`, *optional*):
-                A function that calls at the end of each denoising steps during the inference. The function is called
-                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
-                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
-                `callback_on_step_end_tensor_inputs`.
+                A function that is called at the end of each denoising step during inference. The function is called
+                with the following arguments:
+                    `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`.
+                `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
             callback_on_step_end_tensor_inputs (`List`, *optional*):
                 The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                 will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
@@ -382,26 +407,12 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
                 Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                 plain tuple.
 
+        Examples:
+
         Returns:
             [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`:
                 If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list of list with the generated frames.
-
-        Examples:
-
-        ```py
-        from diffusers import StableVideoDiffusionPipeline
-        from diffusers.utils import load_image, export_to_video
-
-        pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16")
-        pipe.to("cuda")
-
-        image = load_image("https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200")
-        image = image.resize((1024, 576))
-
-        frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0]
-        export_to_video(frames, "generated.mp4", fps=7)
-        ```
+                otherwise a `tuple` of (`List[List[PIL.Image.Image]]` or `np.ndarray` or `torch.FloatTensor`) is returned.
         """
         # 0. Default height and width to unet
         height = height or self.unet.config.sample_size * self.vae_scale_factor
@@ -429,8 +440,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
         # 3. Encode input image
         image_embeddings = self._encode_image(image, device, num_videos_per_prompt, self.do_classifier_free_guidance)
 
-        # NOTE: Stable Diffusion Video was conditioned on fps - 1, which
-        # is why it is reduced here.
+        # NOTE: Stable Video Diffusion was conditioned on fps - 1, which is why it is reduced here.
         # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188
         fps = fps - 1
 
@@ -471,11 +481,11 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
         )
         added_time_ids = added_time_ids.to(device)
 
-        # 4. Prepare timesteps
+        # 6. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps = self.scheduler.timesteps
 
-        # 5. Prepare latent variables
+        # 7. Prepare latent variables
         num_channels_latents = self.unet.config.in_channels
         latents = self.prepare_latents(
             batch_size * num_videos_per_prompt,
@@ -489,7 +499,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
             latents,
         )
 
-        # 7. Prepare guidance scale
+        # 8. Prepare guidance scale
         guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0)
         guidance_scale = guidance_scale.to(device, latents.dtype)
         guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1)
@@ -497,7 +507,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
 
         self._guidance_scale = guidance_scale
 
-        # 8. Denoising loop
+        # 9. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
         self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
@@ -506,7 +516,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
                 latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
-                # Concatenate image_latents over channels dimention
+                # Concatenate image_latents over channels dimension
                 latent_model_input = torch.cat([latent_model_input, image_latents], dim=2)
 
                 # predict the noise residual